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Supervised feature selection via matrix factorization based on singular value decomposition

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 185卷 105-113页

作者： Zare, Masoumeh Eftekhari, Mahdi Aghamollaei, Gholamreza Shahid Bahonar Univ Kerman Mahani Math Res Ctr Box 76135-133 Kerman Iran Shahid Bahonar Univ Kerman Dept Pure Math Fac Math & Comp Kerman Iran Shahid Bahonar Univ Kerman Dept Comp Engn Box 76135-133 Kerman Iran

Feature selection is a main research issue in gene expression data. It tries to determine the best subset of features (genes) for a machine learning model or statistical analysis. In this paper, a novel supervised feature selection algorithm is proposed by using matrix factorization and Singular Value Decomposition (SVD) for microarray datasets. First, an unsupervised matrix factorization based existed method in the literature is used for feature selection as the main framework of our proposed method. Then SVD is incorporated into the first matrix factorization approach for increasing its efficiency. In SVD, a matrix decomposes into three matrices such that one of these factors contains an orthonormal basis for original matrix. Thus, a submatrix of the original matrix is located instead of this factor. The replaced submatrix produces the same space as original matrix, therefore without any complex measure we find a submatrix that has the least distance with data matrix. By considering linear independence as a redundancy criterion, selected features have the least redundancy between themselves. To improve prediction accuracy, the sum of Information Gain (IG) values for selected features is formulated in the objective function as the relevancy measure and makes our algorithm to be a supervised approach. Finally, the proposed algorithm is compared with eleven common feature selection methods and two state-of-the-art approaches using nine publicly available microarray datasets. In terms of accuracy, the experimental results indicate that the proposed method is comparable with the state-of-the-art methods and it has the better performance than the others.

关键词： microarray dataset Supervised feature selection Matrix factorization Singular value decomposition

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A novel control factor and Brownian motion-based improved Harris Hawks Optimization for feature selection

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第7期14卷 8631-8653页

作者： Balakrishnan, K. Dhanalakshmi, R. Khaire, Utkarsh Mahadeo Indian Inst Informat Technol Dept Comp Sci & Engn Tiruchirappalli 620012 Tamil Nadu India Indian Inst Informat Technol Dept Data Sci & Intelligent Syst Dharwad 580009 Karnataka India

The massive growth in data size has prompted proliferation in need for Feature Selection (FS). Hence, FS has become an imperative method for dealing with high-dimensional data. This research critique proposes an enhanced feature selection of Harris Hawks Optimization (HHO) based on the novel control factor and Brownian motion. The Brownian motion augments the exploitation of foragers. It also replicates the deceptive movement of prey, allowing predators to correct their location and direction according to the prey's position. At the same time, the novel control factor imitates the exact behavior of the prey's escaping energy. The comparative analysis with the existing technique using six real high-dimensional microarray datasets highlights the impact of the proposed Improved Harris Hawks Optimization (iHHO). The experimental results of FS and classification accuracy vividly depict how the proposed model outperforms the existing techniques.

关键词： Feature selection Harris Hawks Optimization Meta-heuristic optimization microarray dataset

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A robust ensemble feature selection technique for high-dimensional datasets based on minimum weight threshold method

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COMPUTATIONAL INTELLIGENCE 2022年第5期38卷 1616-1658页

作者： Guney, Huseyin Oztoprak, Huseyin Bahcesehir Cyprus Univ Comp Engn Dept Nicosia North Cyprus Turkey Cyprus Int Univ Elect & Elect Engn Dept Nicosia North Cyprus Turkey

Ensemble feature selection (EFS) is a valuable technique for developing accurate and robust machine-learning (ML) models. Data variation plays a crucial role in the success of EFS models;however, it also causes some outliers in the ranked lists. In this study, we proposed the minimum weight threshold method-based EFS (MWT-EFS) to address the outlier problem and use the true power of EFS. The proposed method employs the support vector classifier to assign weights for features, and the MWT method handles outliers in the ranked feature lists while creating the ensemble list. First, a threshold value is determined. After that, the feature weights below the threshold are replaced with this value. This approach eliminates the negative effect of outliers. After the new feature weights are assigned, the average of the feature weights is calculated (mean aggregation) for all features, and the ensemble (final) feature list is created accordingly. The experiment results showed that the proposed method significantly improves gene selection stability while maintaining classification performance and reducing computational complexity. In conclusion, the proposed method led to an accurate and robust classification that can help domain experts to make more confident decisions with less effort, resources, and time.

关键词： aggregation method ensemble feature selection gene selection microarray dataset support vector machine

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Instigating the Sailfish Optimization Algorithm Based on Opposition-Based Learning to Determine the Salient Features From a High-Dimensional dataset

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2023年第5期22卷 1617-1649页

作者： Khaire, Utkarsh Mahadeo Dhanalakshmi, R. Balakrishnan, K. Akila, M. Indian Inst Informat Technol Dharwad Dept Data Sci & Intelligent Syst Dharwad 580009 Karnataka India Indian Inst Informat Technol Tiruchirappalli Dept Comp Sci & Engn Tiruchirappalli 620012 Tamil Nadu India KPR Inst Engn & Technol Dept Comp Sci & Engn Coimbatore 641407 Tamil Nadu India

The aim of this research critique is to propose a hybrid combination of Opposition-Based Learning and Sailfish Optimization strategy to recognize the salient features from a high-dimensional dataset. The Sailfish Optimization is a swarm-based metaheuristics optimization algorithm inspired by the foraging strategy of a group of Sailfish. Sailfish Optimization explores the search space in only one direction, limiting its converging capacity and causing local minima stagnation. Convergence will be optimal if the search space is reconnoitred in both directions, improving classification accuracy. As a result, combining the Opposition-Based Learning and Sailfish Optimization strategies improves SFO's exploration capability by patrolling the search space in all directions. Sailfish Optimization Algorithm based on Opposition-Based Learning successfully amalgamates the model to global optima at a faster convergence rate and better classification accuracy. The recommended method is tested with six different cancer microarray datasets for two different classifiers: the Support Vector Machine classifier and the K-Nearest Neighbor classifier. From the results obtained, the proposed model aided with Support Vector Machine outperforms the existing Sailfish Optimization with or without K-Nearest Neighbor in terms of convergence capability, classification accuracy, and selection of the most delicate salient features from the dataset.

关键词： Feature selection Opposition-based Learning Sailfish Optimization metaheuristic optimization microarray dataset

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SAGA: A hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks

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JOURNAL OF BIOMEDICAL INFORMATICS 2015年 53卷 27-35页

作者： Adabor, Emmanuel S. Acquaah-Mensah, George K. Oduro, Francis T. Kwame Nkrumah Univ Sci & Technol Dept Math Kumasi Ghana MCPHS Univ Massachusetts Coll Pharm & Hlth Sci Dept Pharmaceut Sci Worcester MA USA

Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Bayesian Network Inference Search algorithms Transcriptional regulatory network microarray dataset

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JNK/MAPK pathway regulation by BEX2 gene silencing in alcoholic hepatitis mice: Effects on oxidative stress

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ALCOHOL-CLINICAL AND EXPERIMENTAL RESEARCH 2023年第10期47卷 1869-1882页

作者： Zhou, Shuai Zhong, Hai Wang, Yong Wang, Xiaoguang Pan, Hongtao Liu, Xiaolin Hu, Lingyu Anhui Med Univ Hosp 2 Dept Gen Surg Hefei Anhui Peoples R China Jiaxing Univ Affiliated Hosp 2 Dept Surg Jiaxing Peoples R China Jiaxing Univ Affiliated Hosp 2 Dept Surg 1518 North Huancheng Rd Jiaxing 314000 Zhejiang Peoples R China

Background: Alcoholic hepatitis (AH) is a severe alcoholic-related liver disease that is a leading cause of morbidity and mortality, for which effective treatments are lacking. Brain-expressed X-linked gene 2 (BEX2) has been implicated in various diseases, but its association with AH has received limited attention. Thus, this study investigated BEX2's impact on the progression of AH by affecting the c-Jun NH2-terminal kinase/mitogen-activated protein kinase (JNK/MAPK) ***: microarray dataset GSE28619 from the Gene Expression Omnibus database was used to identify differentially expressed genes in AH. Immunohistochemistry, terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL), Western blot analysis, and flow cytometry were used to measure various factors in the liver tissue of AH ***: BEX2 expression was significantly upregulated in the model. BEX2 gene silencing increased the levels of glutathione peroxidase and superoxide dismutase while decreasing malondialdehyde content;phosphorylation of JNK, c-JUN, and p38MAPK;apoptosis rate;and the extent of JNK/MAPK pathway ***: These findings provide valuable insights into the mechanisms underlying AH development and highlight the potential role of BEX2 gene expression as a promising therapeutic target for AH.

关键词： alcoholic hepatitis brain expressed X-linked gene 2 (BEX2) gene silencing JNK/MAPK pathway liver tissue microarray dataset oxidative stress

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Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks

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BMC GENOMICS 2015年第Sup3期16卷 S4-S4页

作者： Deng, Su-Ping Zhu, Lin Huang, De-Shuang Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China

Background: Bladder cancer is the most common malignant tumor of the urinary system and it is a heterogeneous disease with both superficial and invasive growth. However, its aetiological agent is still unclear. And it is indispensable to find key genes or modules causing the bladder cancer. Based on gene expression microarray datasets, constructing differential co-expression networks (DCNs) is an important method to investigate diseases and there have been some relevant good tools such as R package 'WGCNA', 'DCGL'. Results: Employing an integrated strategy, 36 up-regulated differentially expressed genes (DEGs) and 356 down-regulated DEGs were selected and main functions of those DEGs are cellular physiological precess(24 up-regulated DEGs;167 down-regulated DEGs) and cellular metabolism (19 up-regulated DEGs;104 down-regulated DEGs). The up-regulated DEGs are mainly involved in the the pathways related to "metabolism". By comparing two DCNs between the normal and cancer states, we found some great changes in hub genes and topological structure, which suggest that the modules of two different DCNs change a lot. Especially, we screened some hub genes of a differential subnetwork between the normal and the cancer states and then do bioinformatics analysis for them. Conclusions: Through constructing and analyzing two differential co-expression networks at different states using the screened DEGs, we found some hub genes associated with the bladder cancer. The results of the bioinformatics analysis for those hub genes will support the biological experiments and the further treatment of the bladder cancer.

关键词： Bladder Cancer microarray dataset Large Relative Difference Topological Overlap Matrice Regulatory Impact Factor

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Characteristics of predictor sets found using differential prioritization

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ALGORITHMS FOR MOLECULAR BIOLOGY 2007年第1期2卷 1-21页

作者： Ooi, Chia Huey Chetty, Madhu Teng, Shyh Wei Monash Univ Gippsland Sch Informat Technol Churchill Vic 3842 Australia

Background: Feature selection plays an undeniably important role in classification problems involving high dimensional datasets such as microarray datasets. For filter-based feature selection, two well-known criteria used in forming predictor sets are relevance and redundancy. However, there is a third criterion which is at least as important as the other two in affecting the efficacy of the resulting predictor sets. This criterion is the degree of differential prioritization (DDP), which varies the emphases on relevance and redundancy depending on the value of the DDP. Previous empirical works on publicly available microarray datasets have confirmed the effectiveness of the DDP in molecular classification. We now propose to establish the fundamental strengths and merits of the DDP-based feature selection technique. This is to be done through a simulation study which involves vigorous analyses of the characteristics of predictor sets found using different values of the DDP from toy datasets designed to mimic real-life microarray datasets. Results: A simulation study employing analytical measures such as the distance between classes before and after transformation using principal component analysis is implemented on toy datasets. From these analyses, the necessity of adjusting the differential prioritization based on the dataset of interest is established. This conclusion is supported by comparisons against both simplistic rank-based selection and state-of-the-art equal-priorities scoring methods, which demonstrates the superiority of the DDP-based feature selection technique. Reapplying similar analyses to real-life multiclass microarray datasets provides further confirmation of our findings and of the significance of the DDP for practical applications. Conclusion: The findings have been achieved based on analytical evaluations, not empirical evaluation involving classifiers, thus providing further basis for the usefulness of the DDP and validating the need for uneq

关键词： Feature Selection Marker Gene microarray dataset Manhattan Distance Feature Selection Technique

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Identification of Differentially Expressed Genes under the Regulation of Transcription Factors in Osteosarcoma

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PATHOLOGY & ONCOLOGY RESEARCH 2019年第3期25卷 1091-1102页

作者： Liu, Yang Guan, Jianzhong Chen, Xiaotian Bengbu Med Coll Affiliated Hosp 1 Dept Orthopaed 287 ChangHuai Rd Bengbu 233004 Anhui Peoples R China

The present study was to investigate and identify the differentially expressed genes (DEGs) in the transcriptional regulatory network of osteosarcoma (OS). The gene expression dataset from Gene Expression Omnibus (GEO) datasets was downloaded. DEGs were identified and their functional annotation was also conducted. In addition, differentially expressed transcription factors (TFs) and the regulatory genes were identified. The electronic validation was used to verify the expression of selected genes. The integrated analysis led to 932 DEGs. The results of functional annotation indicated that these DEGs significantly enriched in the p53 signaling pathway, Jak-STAT signaling pathway and Wnt signaling pathway. ZNF354C, NFIC, NFATC2, SP2, FOXO3, EGR1, ZEB1, RREB1, EGR2 and SRF were covered by most TFs. The expression levels of NFIC and EGR2 in electronic validation were compatible with our bio-informatics result. In conclusion, the deregulation of these genes may provide valuable information in understanding the underlying molecular mechanism in the OS.

关键词： Osteosarcoma microarray dataset Differentialy expressed genes Transcription factors

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Combined genomic expressions as a diagnostic factor for oral squamous cell carcinoma

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GENOMICS 2014年第5-6期103卷 317-322页

作者： Kim, Ki-Yeol Zhang, Xianglan Cha, In-Ho Yonsei Univ Coll Dent Oral Canc Res Inst Seoul 120752 South Korea Yanbian Univ Coll Med Dept Pathol Yanji Jilin Province Peoples R China Yonsei Univ Coll Dent Dept Oral & Maxillofacial Surg Seoul 120752 South Korea

Trends in genetics are transforming in order to identify differential coexpressions of correlated gene expression rather than the significant individual gene. Moreover, it is known that a combined biomarker pattern improves the discrimination of a specific cancer. The identification of the combined biomarker is also necessary for the early detection of invasive oral squamous cell carcinoma (OSCC). To identify the combined biomarker that could improve the discrimination of OSCC, we explored an appropriate number of genes in a combined gene set in order to attain the highest level of accuracy. After detecting a significant gene set, including the pre-defined number of genes, a combined expression was identified using the weights of genes in a gene set. We used the Principal Component Analysis (PCA) for the weight calculation. In this process, we used three public microarray datasets. One dataset was used for identifying the combined biomarker, and the other two datasets were used for validation. The discrimination accuracy was measured by the out-of-bag (OOB) error. There was no relation between the significance and the discrimination accuracy in each individual gene. The identified gene set included both significant and insignificant genes. One of the most significant gene sets in the classification of normal and OSCC included MMP1, SOCS3 and ACOX1. Furthermore, in the case of oral dysplasia and OSCC discrimination, two combined biomarkers were identified. The combined expression revealed good performance in the validation datasets. The combined genomic expression achieved better performance in the discrimination of different conditions than a single significant gene. Therefore, it could be expected that accurate diagnosis for cancer could be possible with a combined biomarker. (C) 2013 Published by Elsevier Inc.

关键词： Oral squamous cell carcinoma Combined biomarker Combined gene expression microarray dataset

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